Robust Sugeno Type Adaptive Fuzzy Neural Network Cotnorllers for Two-Axis Motion Control System

• Main Authors: Po-Huan Chou, 周柏寰
• Other Authors: Faa-Jeng Lin
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/3u778f

碩士 === 國立東華大學 === 電機工程學系 === 95 === The purpose of this thesis is to develop and implement intelligent control systems for a two-axis motion control system, which is composed of two permanent magnet linear synchronous motors (PMLSMs), to achieve precision position control with robustness based on TMS320C32 digital signal processor (DSP). First, the two-axis motion control system, which consists of two field-oriented PMLSM servo drive systems was introduced. Next, a DSP control board is developed to implement the field-oriented mechanisms and the coordinate transfermations. Then, a self-constructing Sugeno type adaptive fuzzy neural network control system, a robust fuzzy neural network controller with nonlinear disturbance observer control system and a robust Sugeno type adaptive fuzzy neural network backstepping control system are developed individually to confront the parameter variations, external disturbances, cross-coupled interference and friction force, and to improve the control performance of the two-axis motion control system for contours tracking. Moreover, the robustness of the servo drive system is verified by putting one iron disk with the mass 20kg on the table of the two-axis motion control system. Furthermore, the feasibility of the proposed control systems are simulated by the “Matlab” package. In addition, the field-oriented mechanisms, the proposed control algorithms and the on-line adaptation laws are realized in the DSP using the C and assembly languages. Finally, the effectiveness of the proposed control schemes is demonstrated by some simulated and experimental results.